Integrated analysis of productivity and machine condition degradation: Performance evaluation and bottleneck identification

Yunyi Kang and Feng Ju

IISE Transactions, 2019, vol. 51, issue 5, 501-516

Abstract: Machine condition degradation is widely observed in manufacturing systems. It has been shown that machines working at different operating states may break down in different probabilistic manners. In addition, machines working in a worse operating stage are more likely to fail, thus causing more frequent down periods and reducing the system throughput. However, there is still a lack of analytical methods to quantify the potential impact of machine condition degradation on the overall system performance to facilitate operation decision making on the factory floor. In this article, we consider a serial production line with finite buffers and multiple machines following Markovian degradation process. An integrated model based on the aggregation method is built to quantify the overall system performance and its interactions with machine condition process. Moreover, system properties are investigated to analyze the influence of system parameters on system performance. In addition, three types of bottlenecks are defined and their corresponding indicators are derived to provide guidelines on improving system performance. These methods provide quantitative tools for modeling, analyzing, and improving manufacturing systems with the coupling between machine condition degradation and productivity.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/24725854.2018.1494867 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:uiiexx:v:51:y:2019:i:5:p:501-516

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/uiie20

DOI: 10.1080/24725854.2018.1494867

Access Statistics for this article

IISE Transactions is currently edited by Jianjun Shi

More articles in IISE Transactions from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().